/* * Copyright (c) 2021 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "helpers.h" #include "helpers_asymm.h" /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32 * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1 * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * @note The output quantization multiplier must be passed at compile time using -DOUTPUT_MULTIPLIER e.g. -DOUTPUT_MULTIPLIER=1234 * @note The output quantization shift must be passed at compile time using -DOUTPUT_SHIFT e.g. -DOUTPUT_SHIFT=4 * @note The input offset quantization parameter must be passed at compile time using -DINPUT_OFFSET e.g. -DINPUT_OFFSET=3 * @note The weights offset quantization parameter must be passed at compile time using -DWEIGHTS_OFFSET e.g. -DWEIGHTS_OFFSET=3 * * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension */ __kernel void direct_convolution_nchw( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights), #ifdef HAS_BIAS VECTOR_DECLARATION(biases), #endif /* defined(HAS_BIAS) */ unsigned int weights_stride_w) { const int id0 = get_global_id(0); const int id1 = get_global_id(1); const int id2 = get_global_id(2); const int x_coords = (id0 * STRIDE_X) - PAD_LEFT; const int y_coords = (id1 * STRIDE_Y) - PAD_TOP; const int x_offs = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0) * sizeof(DATA_TYPE); __global uchar *src_addr = (__global uchar *)(src_ptr + src_offset_first_element_in_bytes); __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + id2 * weights_stride_w); __global uchar *dst_addr = (__global uchar *)dst_ptr + dst_offset_first_element_in_bytes + x_offs + id1 * dst_stride_y + id2 * dst_stride_z; #ifdef IS_QUANTIZED int acc_value = 0; #else /* IS_QUANTIZED */ DATA_TYPE acc_value = 0; #endif /* IS_QUANTIZED */ for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) { for(int y = 0; y < WEI_HEIGHT; ++y) { for(int x = 0; x < WEI_WIDTH; ++x) { const int idx_x = (x_coords + x); const int idx_y = (y_coords + y); if((idx_x >= 0 && idx_x < SRC_WIDTH) && (idx_y >= 0 && idx_y < SRC_HEIGHT)) { const int weight_offset = x + (WEI_HEIGHT * y); const int input_offset = idx_x + SRC_WIDTH * idx_y; #ifdef IS_QUANTIZED int weight = convert_int(*((__global DATA_TYPE *)weights_addr + weight_offset)); int input = convert_int(*((__global DATA_TYPE *)src_addr + input_offset)); acc_value += (input + INPUT_OFFSET) * (weight + WEIGHTS_OFFSET); #else /* IS_QUANTIZED */ DATA_TYPE weight = *((__global DATA_TYPE *)weights_addr + weight_offset); DATA_TYPE input = *((__global DATA_TYPE *)src_addr + input_offset); acc_value += input * weight; #endif /* IS_QUANTIZED */ } } } src_addr += src_stride_z; weights_addr += weights_stride_z; } #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #ifdef IS_QUANTIZED int bias = *((__global int *)(vector_offset(&biases, id2))); #else /* IS_QUANTIZED */ DATA_TYPE bias = *((__global DATA_TYPE *)(vector_offset(&biases, id2))); #endif /* IS_QUANTIZED */ acc_value += bias; #endif /* defined(HAS_BIAS) */ #ifdef IS_QUANTIZED #if OUTPUT_SHIFT < 0 acc_value = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(acc_value, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 1); #else // OUTPUT_SHIFT < 0 acc_value = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(acc_value, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 1); #endif // OUTPUT_SHIFT < 0 acc_value = acc_value + OUTPUT_OFFSET; #endif /* IS_QUANTIZED */ *(__global DATA_TYPE *)dst_addr = CONVERT_SAT(acc_value, DATA_TYPE); }